Detecting Intruders in Network using Machine Learning

Abstract

Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Malicious software applications, or malware, are the primary source of many security problems. These intentionally manipulative malicious applications intend to perform unauthorized activities on behalf of their originators on the host machines for various reasons such as stealing advanced technologies and intellectual properties, governmental acts of revenge, and tampering sensitive information, to name a few. Malware detection methods rely on signature databases, including malicious instruction patterns in today's practice. The signature databases are used for matching against a signature generated from a newly encountered executable. Nevertheless, more efficient mitigation methods are needed due to the fast expansion of malicious software on the Internet and their self- modifying abilities, as in polymorphic and metamorphic malware. In this work, it detects Network Intrusion anomalies by using NSL-KDD dataset. The user enters the hacking parameters in the front end. The model predicts the type of attack and gives information about the type of attack to the user. The project is fully responsive and completely based on session and cookies (Client-server protocol). Then we activated our malware security device which forms production for the set of attack. This will help many cyber threads